
#import packages
import pandas as pd
import scipy as sp, numpy as np
from sklearn import preprocessing, model_selection
from sklearn.preprocessing import scale, StandardScaler, normalize, Normalizer
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import PLSRegression
from sklearn.model_selection import ShuffleSplit, LeaveOneGroupOut, LeaveOneOut, train_test_split, learning_curve, GridSearchCV, cross_val_score, cross_val_predict
import matplotlib.pyplot as plt
import scipy.io as io
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import cross_val_score 
from sklearn.linear_model import LinearRegression
import warnings

#error handling
warnings.filterwarnings('ignore')


#define functions

def display_scores(scores): 
    i_mean[i-1] = scores.mean()
    i_std[i-1] = scores.std()
    return i_mean, i_std

def moving_average(a, n) :
    ret = np.cumsum(a, dtype=float)
    ret[n:] = ret[n:] - ret[:-n]
    return ret[n - 1:] / n

def abline(slope, intercept):
    """Plot a line from slope and intercept"""
    axes = plt.gca()
    x_vals = np.array(axes.get_xlim())
    y_vals = intercept + slope * x_vals
    plt.plot(x_vals, y_vals, '--')

def vip(model):
    t = model.x_scores_
    w = model.x_weights_
    q = model.y_loadings_
    p, h = w.shape
    vips = np.zeros((p,))
    s = np.diag(t.T @ t @ q.T @ q).reshape(h, -1)
    total_s = np.sum(s)
    for i in range(p):
        weight = np.array([ (w[i,j] / np.linalg.norm(w[:,j]))**2 for j in range(h) ])
        vips[i] = np.sqrt(p*(s.T @ weight)/total_s)
    return vips

# Learning Curves
#cross validation

def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,
                        n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):
    """
    Generate 3 plots: the test and training learning curve, the training
    samples vs fit times curve, the fit times vs score curve.

    Parameters
    ----------
    estimator : estimator instance
        An estimator instance implementing `fit` and `predict` methods which
        will be cloned for each validation.

    title : str
        Title for the chart.

    X : array-like of shape (n_samples, n_features)
        Training vector, where ``n_samples`` is the number of samples and
        ``n_features`` is the number of features.

    y : array-like of shape (n_samples) or (n_samples, n_features)
        Target relative to ``X`` for classification or regression;
        None for unsupervised learning.

    axes : array-like of shape (3,), default=None
        Axes to use for plotting the curves.

    ylim : tuple of shape (2,), default=None
        Defines minimum and maximum y-values plotted, e.g. (ymin, ymax).

    cv : int, cross-validation generator or an iterable, default=None
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

          - None, to use the default 5-fold cross-validation,
          - integer, to specify the number of folds.
          - :term:`CV splitter`,
          - An iterable yielding (train, test) splits as arrays of indices.

        For integer/None inputs, if ``y`` is binary or multiclass,
        :class:`StratifiedKFold` used. If the estimator is not a classifier
        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validators that can be used here.

    n_jobs : int or None, default=None
        Number of jobs to run in parallel.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    train_sizes : array-like of shape (n_ticks,)
        Relative or absolute numbers of training examples that will be used to
        generate the learning curve. If the ``dtype`` is float, it is regarded
        as a fraction of the maximum size of the training set (that is
        determined by the selected validation method), i.e. it has to be within
        (0, 1]. Otherwise it is interpreted as absolute sizes of the training
        sets. Note that for classification the number of samples usually have
        to be big enough to contain at least one sample from each class.
        (default: np.linspace(0.1, 1.0, 5))
    """
    if axes is None:
        _, axes = plt.subplots(1, 3, figsize=(20, 5))

    axes[0].set_title(title)
    if ylim is not None:
        axes[0].set_ylim(*ylim)
    axes[0].set_xlabel("Training examples")
    axes[0].set_ylabel("Score")

    train_sizes, train_scores, test_scores, fit_times, _ = \
        learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,
                       train_sizes=train_sizes,
                       return_times=True)
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    fit_times_mean = np.mean(fit_times, axis=1)
    fit_times_std = np.std(fit_times, axis=1)

    # Plot learning curve
    axes[0].grid()
    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,
                         train_scores_mean + train_scores_std, alpha=0.1,
                         color="r")
    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,
                         test_scores_mean + test_scores_std, alpha=0.1,
                         color="g")
    axes[0].plot(train_sizes, train_scores_mean, 'o-', color="r",
                 label="Training score")
    axes[0].plot(train_sizes, test_scores_mean, 'o-', color="g",
                 label="Cross-validation score")
    axes[0].legend(loc="best")

    # Plot n_samples vs fit_times
    axes[1].grid()
    axes[1].plot(train_sizes, fit_times_mean, 'o-')
    axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,
                         fit_times_mean + fit_times_std, alpha=0.1)
    axes[1].set_xlabel("Training examples")
    axes[1].set_ylabel("fit_times")
    axes[1].set_title("Scalability of the model")

    # Plot fit_time vs score
    axes[2].grid()
    axes[2].plot(fit_times_mean, test_scores_mean, 'o-')
    axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,
                         test_scores_mean + test_scores_std, alpha=0.1)
    axes[2].set_xlabel("fit_times")
    axes[2].set_ylabel("Score")
    axes[2].set_title("Performance of the model")

    return plt


class Sample:
    instances=[]
    """Sample representing in vivo voltammograms"""
    def __init__(self,myName=0,myData=0,myTime=0,myStimType=0,myStimLength=0,myStimFreq=0,myPosition=0,myBehavior=0,myModel=0,
                myNumComp=0,myPreProcess = 0, myHyperParams = 0,mySampleName=0,myDrug=0,myAlignedData=0, myBrainRegion=0,
                my5HTbasal=0,my5HTphasic=0,myDAbasal=0,myDAphasic=0):
        self._Name = myName
        self._Data = myData
        self._Time = myTime 
        self._StimType = myStimType
        self._StimLength = myStimLength
        self._StimFreq = myStimFreq
        self._Position = myPosition
        self._Behavior = myBehavior
        self._Model = myModel
        self._NumComp = myNumComp
        self._PreProcess = myPreProcess
        self._HyperParams = myHyperParams
        self._SampleName = mySampleName
        self._Drug = myDrug
        self._AlignedData = myAlignedData
        self._BrainRegion = myBrainRegion
        self._5HT_Basal = my5HTbasal
        self._5HT_Phasic = my5HTphasic
        self._DA_Basal = myDAbasal
        self._DA_Phasic = myDAphasic
        Sample.instances.append(self)
    
    def set_StimFreq(self,stimFreq):
        self._StimFreq = stimFreq
    
    def set_StimLength(self,stimLength):
        self._StimLength = stimLength
    
    def set_Position(self,position):
        self._Position = position
    
    def set_Drug(self,drug):
        self._Drug = drug


#insert file path below
file_location=r'C:\Users\csmov\fscv_fpv_HYdata\in vivo Data FSCV_FPV_2020\in vivo Data FSCV_FPV_2020\Master_Combined\S1459_S1447_S1504_Combined.xlsx'
sheetName = 'NBS NA PulseCut' #BS or NBS, NA or 25A, PulseCut or TriCut

#Hyperparameters
nComponents = 2

#Preprocessing
preProcess = 'Normalize' #'No Scale', 'Scale Features', 'Scale Samples', or 'Normalize'
myNorm = 'max' #'max', 'l2' or 'l1'

#Cross-validation
cvFolds = 18 

#Model
modelChoice = 'PCR' #'PLSR', 'PCR'


#import data
df_RPV = pd.read_excel(file_location, sheet_name=sheetName) #dataframe for voltammogram response
df_RPV_T = df_RPV.T #transpose to get in correct format for sklearn
df_ConcAll = pd.read_excel(file_location, sheet_name = 'Conc') #dataframe for concentrations of df_RPV responses
df_ConcDA = df_ConcAll[["DA"]] # contains only dopamine (DA) concentrations
df_ConcHT = df_ConcAll[["HT"]] # contains only serotonin (5HT) concentrations
df_Samples = pd.DataFrame((list(df_RPV.columns)),columns=['Sample']) #extracts sample code

X = df_RPV_T.to_numpy();
X_raw = df_RPV_T.to_numpy();
y = df_ConcAll.to_numpy();
y_DA = df_ConcAll['DA'].to_numpy();
y_5HT = df_ConcAll['HT'].to_numpy();

list_Samples = df_Samples.values.tolist();

plt.plot(X.T);
plt.xlabel("Sample");
plt.ylabel("Current (nA)");
plt.title("Overlaid Voltammograms");


#preprocess

if preProcess == 'Scale Features':
    preProcesser = preprocessing.StandardScaler().fit(X) 
    plt.ylabel("Scaled Current (nA)");
    plt.title("Scaled Feature Voltammograms");
    
if preProcess == 'Normalize':
    preProcesser = preprocessing.Normalizer(norm=myNorm).fit(X) 
    plt.ylabel("Normalized Current (nA)");
    plt.title("Normalized Voltammograms");
    
if preProcess == 'No Scale':
    preProcesser = preprocessing.StandardScaler(with_mean=False,with_std=False).fit(X) 
    plt.ylabel("Scaled Current (nA)");
    plt.title("Non-scaled Voltammograms");
    
if preProcess == 'Scale Samples':
    preProcesser = preprocessing.StandardScaler().fit(X.T)
    plt.ylabel("Scaled Current (nA)");
    plt.title("Scaled Sample Voltammograms");
    
X = preProcesser.transform(X) 
plt.plot(X.T);
plt.xlabel("Sample");


#PCA of Y
#ignore warnings; may need to update get_value in future.
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)

pca = PCA(n_components = 2) 
principalComponents = pca.fit_transform(df_ConcAll)
principalDf = pd.DataFrame(data = principalComponents, columns = ['principal component 1', 'principal component 2'])
finalDf = pd.concat([principalDf, df_Samples], axis = 1)

# plot and label the data in PC space
for i in range(len(df_Samples)):
    plt.scatter(finalDf.get_value(i,'principal component 1'),finalDf.get_value(i,'principal component 2'))
    plt.annotate(finalDf.get_value(i,"Sample"), (finalDf.get_value(i,'principal component 1'),finalDf.get_value(i,'principal component 2')))
plt.plot([-5,0,5],[0,0,0],c='k')
plt.plot([0,0,0],[-3,0,3],c='k')
plt.title("PCs in 2D")
plt.xlabel("PC1")
plt.ylabel("PC2")

print("PC1 % Explained Variance:", round(pca.explained_variance_ratio_[0]*100,2), "\nPC2 % Explained Variance:", round(pca.explained_variance_ratio_[1]*100,2))

PC1 % Explained Variance: 60.72 
PC2 % Explained Variance: 39.28


#PCA of X
#ignore warnings; may need to update get_value in future.
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)

pca = PCA(n_components = 2) 
principalComponents = pca.fit_transform(X)
principalDf = pd.DataFrame(data = principalComponents, columns = ['principal component 1', 'principal component 2'])
finalDf = pd.concat([principalDf, df_Samples], axis = 1)

# plot and label the data in PC space
for i in range(len(df_Samples)):
    plt.scatter(finalDf.get_value(i,'principal component 1'),finalDf.get_value(i,'principal component 2'))
    plt.annotate(finalDf.get_value(i,"Sample"), (finalDf.get_value(i,'principal component 1'),finalDf.get_value(i,'principal component 2')))

plt.title("PCs in 2D")
plt.xlabel("PC1")
plt.ylabel("PC2")

print("PC1 % Explained Variance:",round(pca.explained_variance_ratio_[0]*100,2), "\nPC2 % Explained Variance:", round(pca.explained_variance_ratio_[1]*100,2))

pca = PCA(n_components = 0.95) 
pca.fit(X)
cumsum = np.cumsum(pca.explained_variance_ratio_)
print("Num Components for 95% Variance Explained:", pca.n_components_)
print("PC1 % Explained Variance:", np.round(cumsum,3)[0],"\nPC2 % Explained Variance:", np.round(cumsum,3)[1])

PC1 % Explained Variance: 78.85 
PC2 % Explained Variance: 14.13
Num Components for 95% Variance Explained: 3
PC1 % Explained Variance: 0.789 
PC2 % Explained Variance: 0.93


var_exp_DA = np.zeros(len(df_RPV_T))
var_exp_HT = np.zeros(len(df_RPV_T))
var_exp_All = np.zeros(len(df_RPV_T))
i_values = np.arange(1,len(df_RPV_T)+1)

for i in i_values:
    if modelChoice == 'PLSR':
        myModel = make_pipeline(preProcesser, PLSRegression(n_components=i,scale=False))
        myModel.fit_transform(X, y)
    if modelChoice == 'PCR':
        myModel = make_pipeline(preProcesser, PCA(n_components=i), LinearRegression()) 
        myModel.fit(X, y)
    y_pred = myModel.predict(X)
    var_exp_DA[i-1] = r2_score(y,y_pred,multioutput='raw_values')[0]
    var_exp_HT[i-1] = r2_score(y,y_pred,multioutput='raw_values')[1]
    var_exp_All[i-1] = r2_score(y,y_pred)

df_var_exp_All=pd.DataFrame({"Number Components": i_values, "Variance Explained (%), All": np.round(100*var_exp_All,2)})
print(df_var_exp_All,'\n');
df_var_exp_DA=pd.DataFrame({"Number Components": i_values, "Variance Explained (%), DA": np.round(100*var_exp_DA,2)})
print(df_var_exp_DA,'\n');
df_var_exp_HT=pd.DataFrame({"Number Components": i_values, "Variance Explained (%), HT": np.round(100*var_exp_HT,2)})
print(df_var_exp_HT);

plt.plot(i_values,var_exp_All,label='All');
plt.plot(i_values,var_exp_HT,label="HT");
plt.plot(i_values,var_exp_DA,label='DA');
plt.legend()
plt.title("Variance Explained by N Components");
plt.xlabel("Number Components");
plt.ylabel("Variance Explained");

    Number Components  Variance Explained (%), All
0                   1                         0.92
1                   2                        35.63
2                   3                        42.08
3                   4                        79.59
4                   5                        87.56
5                   6                        90.06
6                   7                        90.64
7                   8                        94.53
8                   9                        96.03
9                  10                        96.06
10                 11                        96.82
11                 12                        97.38
12                 13                        97.52
13                 14                        99.00
14                 15                        99.38
15                 16                        99.50
16                 17                       100.00
17                 18                       100.00 

    Number Components  Variance Explained (%), DA
0                   1                        0.02
1                   2                       68.89
2                   3                       76.00
3                   4                       83.63
4                   5                       85.16
5                   6                       86.80
6                   7                       86.80
7                   8                       93.80
8                   9                       95.40
9                  10                       95.43
10                 11                       96.21
11                 12                       96.68
12                 13                       96.85
13                 14                       99.38
14                 15                       99.42
15                 16                       99.67
16                 17                      100.00
17                 18                      100.00 

    Number Components  Variance Explained (%), HT
0                   1                        1.82
1                   2                        2.37
2                   3                        8.17
3                   4                       75.55
4                   5                       89.96
5                   6                       93.32
6                   7                       94.48
7                   8                       95.27
8                   9                       96.67
9                  10                       96.69
10                 11                       97.42
11                 12                       98.08
12                 13                       98.18
13                 14                       98.62
14                 15                       99.34
15                 16                       99.34
16                 17                      100.00
17                 18                      100.00


i_mean = np.zeros(len(df_RPV_T)-1)
i_std =np.zeros(len(df_RPV_T)-1)

for i in i_values[:-1]:
    if modelChoice == 'PLSR':
        myModel = make_pipeline(preProcesser, PLSRegression(n_components=i,scale=False))
    if modelChoice == 'PCR':
        myModel = make_pipeline(preProcesser, PCA(n_components=i), LinearRegression())      
    scores = cross_val_score(myModel, X, y, scoring="neg_mean_squared_error", cv=cvFolds)
    model_rmse_scores = np.sqrt(-scores)
    display_scores(model_rmse_scores)
    plt.show()

plt.plot(i_values[:-1],i_mean)
plt.xlabel("Number Components");
plt.ylabel("Mean Scores (RMSE)");
plt.show()
plt.plot(i_values[:-1],i_std)
plt.xlabel("Number Components");
plt.ylabel("Std Dev");


if modelChoice == 'PLSR':
    PLSRmodel=PLSRegression(n_components=nComponents,scale=False) #TODO
    PLSRmodel.fit_transform(X,y)
    vipPLSR = vip(PLSRmodel)
    plt.plot(vip(PLSRmodel))
    plt.xlabel("Feature")
    plt.ylabel('VIP Score')
    plt.title('VIP Scores')
    plt.show()
    plt.plot(df_RPV)
    plt.xlabel("Feature")
    plt.ylabel('Current (nA)')
    plt.title('Voltammogram')
    plt.show()

    countOver1 = 0
    for i in range(len(vipPLSR)):
        if vipPLSR[i] >= 1:
            countOver1 += 1

    print('Number Features with VIP > or = 1:',countOver1)

    plt.plot(moving_average(vipPLSR,n=10))
    plt.xlabel("Feature")
    plt.ylabel('Moving Average VIP Score')
    plt.title('Moving Average of VIP Scores')
    abline(0,1)


#TODO: Set grid search params for PLSR and PCR

if preProcess == 'Scale Features' and modelChoice == 'PLSR':
    param_grid = [
    {'plsregression__n_components': range(len(df_RPV_T)),'standardscaler__with_mean': [True,False],
     'standardscaler__with_std': [True,False]}]

if preProcess == 'Scale Features' and modelChoice == 'PCR':
    param_grid = [
    {'pca__n_components': range(len(df_RPV_T)),'standardscaler__with_mean': [True,False],
     'standardscaler__with_std': [True,False]}]
    
if preProcess == 'No Scale' and modelChoice == 'PLSR':
    param_grid = [
    {'plsregression__n_components': range(len(df_RPV_T))}]
    
if preProcess == 'No Scale' and modelChoice == 'PCR':
    param_grid = [
    {'pca__n_components': range(len(df_RPV_T)),'linearregression__normalize':[True,False]}] 
    
if preProcess == 'Scale Samples' and modelChoice == 'PLSR':
    param_grid = [
    {'plsregression__n_components': range(len(df_RPV_T)), 'plsregression__scale':[True, False]}]

if preProcess == 'Scale Samples' and modelChoice == 'PCR':
    param_grid = [
    {'pca__n_components': range(len(df_RPV_T)), 'linearregression__normalize':[True, False]}]

if preProcess == 'Normalize' and modelChoice == 'PLSR':
    param_grid = [
    {'plsregression__n_components': range(len(df_RPV_T)),'normalizer__norm': ['l1', 'l2', 'max']}]

if preProcess == 'Normalize' and modelChoice == 'PCR':
    param_grid = [
    {'pca__n_components': range(len(df_RPV_T)),'normalizer__norm': ['l1', 'l2', 'max']}]

if modelChoice == 'PLSR':
    GSmodel = make_pipeline(preProcesser, PLSRegression(scale=False)) 
if modelChoice == 'PCR':
    GSmodel = make_pipeline(preProcesser, PCA(), LinearRegression())
    
grid_search = GridSearchCV(GSmodel, param_grid, cv=cvFolds,
        scoring='neg_mean_squared_error',
        return_train_score=True)

grid_search.fit(X,y);

print(grid_search.best_params_, grid_search.best_estimator_)

{'normalizer__norm': 'max', 'pca__n_components': 9} Pipeline(memory=None,
         steps=[('normalizer', Normalizer(copy=True, norm='max')),
                ('pca',
                 PCA(copy=True, iterated_power='auto', n_components=9,
                     random_state=None, svd_solver='auto', tol=0.0,
                     whiten=False)),
                ('linearregression',
                 LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,
                                  normalize=False))],
         verbose=False)


for cvFold in list(range(2,cvFolds+1)):
    CV_var_exp_DA = np.zeros(len(df_RPV_T))
    CV_var_exp_HT = np.zeros(len(df_RPV_T))
    CV_var_exp_All = np.zeros(len(df_RPV_T))
    print(cvFold)
    for i in 2,3,5:
        if modelChoice == 'PLSR':
            myModel = make_pipeline(preProcesser, PLSRegression(n_components=i,scale=False))
        if modelChoice == 'PCR':
            myModel = make_pipeline(preProcesser, PCA(n_components=i), LinearRegression())  
        y_train_pred = cross_val_predict(myModel, X_raw, y, cv=cvFold)
        CV_var_exp_DA[i-1] = r2_score(y_DA,y_train_pred[:,0])
        CV_var_exp_HT[i-1] = r2_score(y_5HT,y_train_pred[:,1])
        CV_var_exp_All[i-1] = r2_score(y,y_train_pred)
    df_CV_var_exp_All=pd.DataFrame({"Number Components": i_values, "Variance Explained (%), All CV": np.round(100*CV_var_exp_All,2)})
    print(df_CV_var_exp_All,'\n');
    df_CV_var_exp_DA=pd.DataFrame({"Number Components": i_values, "Variance Explained (%), DA CV": np.round(100*CV_var_exp_DA,2)})
    print(df_CV_var_exp_DA,'\n');
    df_CV_var_exp_HT=pd.DataFrame({"Number Components": i_values, "Variance Explained (%), HT CV": np.round(100*CV_var_exp_HT,2)})
    print(df_CV_var_exp_HT);
    plt.plot(i_values,CV_var_exp_All,label='All CV');
    plt.plot(i_values,CV_var_exp_HT,label="HT CV");
    plt.plot(i_values,CV_var_exp_DA,label='DA CV');
    plt.legend()
    plt.title("Variance Explained by N Components during "+str(cvFold)+" Fold CV");
    plt.xlabel("Number Components");
    plt.ylabel("Variance Explained");
    plt.show()

2
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                          -22.48
2                   3                          -99.99
3                   4                            0.00
4                   5                          -71.64
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          39.47
2                   3                          26.83
3                   4                           0.00
4                   5                        -177.24
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -84.42
2                   3                        -226.81
3                   4                           0.00
4                   5                          33.95
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

3
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                           24.98
2                   3                         -183.47
3                   4                            0.00
4                   5                            4.29
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          54.58
2                   3                          24.82
3                   4                           0.00
4                   5                           2.96
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                          -4.62
2                   3                        -391.75
3                   4                           0.00
4                   5                           5.61
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

4
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                            8.43
2                   3                           10.31
3                   4                            0.00
4                   5                           66.09
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          47.83
2                   3                          53.61
3                   4                           0.00
4                   5                          61.37
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -30.98
2                   3                         -33.00
3                   4                           0.00
4                   5                          70.82
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

5
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                          -12.44
2                   3                          -13.43
3                   4                            0.00
4                   5                           59.95
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          38.19
2                   3                          49.34
3                   4                           0.00
4                   5                          64.62
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -63.06
2                   3                         -76.19
3                   4                           0.00
4                   5                          55.27
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

6
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                          -17.95
2                   3                          -28.66
3                   4                            0.00
4                   5                           58.45
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          39.97
2                   3                          42.32
3                   4                           0.00
4                   5                          62.38
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -75.86
2                   3                         -99.64
3                   4                           0.00
4                   5                          54.53
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

7
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                            0.95
2                   3                           -6.81
3                   4                            0.00
4                   5                           65.73
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          46.97
2                   3                          44.55
3                   4                           0.00
4                   5                          58.92
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -45.07
2                   3                         -58.17
3                   4                           0.00
4                   5                          72.54
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

8
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                            3.97
2                   3                           -4.95
3                   4                            0.00
4                   5                           66.80
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          48.24
2                   3                          45.77
3                   4                           0.00
4                   5                          57.95
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -40.30
2                   3                         -55.67
3                   4                           0.00
4                   5                          75.65
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

9
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                            5.91
2                   3                            1.37
3                   4                            0.00
4                   5                           67.83
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          48.19
2                   3                          47.85
3                   4                           0.00
4                   5                          57.86
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -36.37
2                   3                         -45.10
3                   4                           0.00
4                   5                          77.79
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

10
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                            6.57
2                   3                            2.28
3                   4                            0.00
4                   5                           66.25
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          50.64
2                   3                          50.51
3                   4                           0.00
4                   5                          55.94
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -37.50
2                   3                         -45.95
3                   4                           0.00
4                   5                          76.56
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00

11
    Number Components  Variance Explained (%), All CV
0                   1                            0.00
1                   2                            3.33
2                   3                           -2.75
3                   4                            0.00
4                   5                           64.86
5                   6                            0.00
6                   7                            0.00
7                   8                            0.00
8                   9                            0.00
9                  10                            0.00
10                 11                            0.00
11                 12                            0.00
12                 13                            0.00
13                 14                            0.00
14                 15                            0.00
15                 16                            0.00
16                 17                            0.00
17                 18                            0.00 

    Number Components  Variance Explained (%), DA CV
0                   1                           0.00
1                   2                          50.49
2                   3                          50.21
3                   4                           0.00
4                   5                          57.01
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00 

    Number Components  Variance Explained (%), HT CV
0                   1                           0.00
1                   2                         -43.84
2                   3                         -55.71
3                   4                           0.00
4                   5                          72.70
5                   6                           0.00
6                   7                           0.00
7                   8                           0.00
8                   9                           0.00
9                  10                           0.00
10                 11                           0.00
11                 12                           0.00
12                 13                           0.00
13                 14                           0.00
14                 15                           0.00
15                 16                           0.00
16                 17                           0.00
17                 18                           0.00


#R2X
if modelChoice == 'PCR':
    pca = PCA(n_components=nComponents);
    X_reduced = pca.fit_transform(X);
    X_recovered = pca.inverse_transform(X_reduced);
    
if modelChoice == 'PLSR':
    plsr = PLSRegression(n_components=nComponents,scale=False);
    X_reduced = plsr.fit_transform(X,y);
    X_recovered = plsr.inverse_transform(plsr.x_scores_);
    
plt.plot(X.T);
plt.xlabel('Sample')
plt.ylabel('Preprocessed Current (nA)')
plt.title('Preprocessed Voltammogram')
plt.show();
plt.plot((X_recovered).T);
plt.xlabel('Sample')
plt.ylabel('Preprocessed Current (nA)')
plt.title('Reconstructed Preprocessed Voltammogram')
plt.show()

if preProcess != 'Normalize':
    plt.plot((preProcesser.inverse_transform(X)).T);
    plt.xlabel('Sample')
    plt.ylabel('Current (nA)')
    plt.title('Voltammogram')
    plt.show();
    plt.plot((preProcesser.inverse_transform(X_recovered)).T);
    plt.xlabel('Sample')
    plt.ylabel('Current (nA)')
    plt.title('Reconstructed Voltammogram')
    plt.show()

print("Reconstruction Error (%):", 100*round(1-r2_score(X,X_recovered),4))

Reconstruction Error (%): 15.85


#R2Y
if modelChoice =='PCR':
    myModel.fit(X_raw,y)
if modelChoice =='PLSR':
    myModel.fit_transform(X_raw,y)
y_pred = myModel.predict(X_raw)
df_PredConc=pd.DataFrame({"Predicted DA": np.round(y_pred[:,0],2),'Actual DA':np.round(y[:,0],2),
                     "Predicted 5HT": np.round(y_pred[:,1],2),'Actual 5HT':np.round(y[:,1],2)})
print(df_PredConc)

r2_score_DA = r2_score(y_DA,y_pred[:,0],multioutput='raw_values')
r2_score_5HT = r2_score(y_5HT,y_pred[:,1],multioutput='raw_values')
print('R2Y DA:',(100*np.round(r2_score_DA,4)))
print('R2Y 5HT:', (100*np.round(r2_score_5HT,4)))
print('R2Y All:',100*np.round(r2_score(y,y_pred),4))

plt.scatter(df_ConcAll["DA"],y_pred[:,0], label="DA");
plt.scatter(df_ConcAll["HT"],y_pred[:,1], label="HT");
plt.xlabel('Observed Concentration (uM)')
plt.ylabel('Predicted Concentration (uM)')
plt.title("Predicted versus Observed Concentration")
abline(1,0)
plt.plot();
plt.legend();

#debug code
R2Y_DA = 1-(np.sum((y_pred[:,0]-y_DA)**2))/(np.sum((y_DA-np.mean(y_DA))**2))
R2Y_5HT = 1-(np.sum((y_pred[:,1]-y_5HT)**2))/(np.sum((y_5HT-np.mean(y_5HT))**2))
assert R2Y_DA == r2_score_DA
assert R2Y_5HT == r2_score_5HT

    Predicted DA  Actual DA  Predicted 5HT  Actual 5HT
0           0.32        0.0          -0.51         0.0
1           3.65        5.0           0.34         0.0
2           0.41        0.0          -0.35         0.0
3          -0.02        0.0           3.28         4.0
4          -0.14        0.0           0.14         0.0
5           3.34        2.0           1.46         2.0
6          -0.01        0.0          -0.06         0.0
7           3.28        4.0           0.11         0.0
8          -0.09        0.0          -0.07         0.0
9          -0.48        0.0           3.86         3.0
10         -0.14        0.0           0.09         0.0
11          2.45        1.5           2.14         1.5
12          0.01        0.0           0.55         0.0
13          4.05        5.0           0.03         0.0
14         -0.05        0.0           0.89         0.0
15         -0.62        0.0           4.33         5.0
16          0.50        0.0          -0.44         0.0
17          3.03        2.0           1.70         2.0
R2Y DA: [85.16]
R2Y 5HT: [89.96]
R2Y All: 87.56


fig, axes = plt.subplots(3, 2, figsize=(10, 15))

title = "Learning Curves (PLSR)"
# Cross validation with 100 iterations to get smoother mean test and train
# score curves, each time with 20% data randomly selected as a validation set.
cv = ShuffleSplit(n_splits=10, test_size=0.1, random_state=0)

estimator = myModel
plot_learning_curve(estimator, title, X_raw, y, axes=axes[:, 0], ylim=(-1, 1.01),
                    cv=cv, n_jobs=4)

plt.show()


# in vivo
time_array = np.arange(0,492)
time_array = (time_array - 200)*1*400/1000
time_array_500 = (np.arange(0,501)-200)*400/1000

#for P1 TP4
stimulationFiles = ['\\25800_P1','\\27800_P1','\\33850_P1',
                    '\\9300_P1_2','\\11250_P1_2','\\13180_P1_2','\\16280_P1_2','\\18000_P1_2','\\22080_P1_2','\\23800_P1_2','\\26400_P1_2','\\31900_P1_2',
                    '\\20100_postinj','\\22050_postinj','\\23950_postinj','\\28200_postinj','\\31550_postinj','\\33350_postinj','\\34900_postinj']
#stimulationFiles= ['\\14950_postESC', '\\17030_postESC']
nameFiles = []
for i in stimulationFiles:
    nameFiles.append('Obj'+i[1:])
print(nameFiles)

objs = list()
current_dfs=[]
for x in [2]:
    print(x)
    if modelChoice == 'PLSR':
        myModel = make_pipeline(preProcesser, PLSRegression(n_components=x,scale=False))
        myModel.fit_transform(X_raw,y)
    if modelChoice == 'PCR':
        myModel = make_pipeline(preProcesser, PCA(n_components=x), LinearRegression())          
        myModel.fit(X_raw,y)
    j=0
    for i in stimulationFiles:
        #iv_file_location=r'C:\Users\csmov\fscv_fpv_HYdata\in vivo Data FSCV_FPV_2020\in vivo Data FSCV_FPV_2020\20190112'
        iv_file_location=r'C:\Users\csmov\fscv_fpv_HYdata\in vivo Data FSCV_FPV_2020\in vivo Data FSCV_FPV_2020\20190113'
        iv_file_location=iv_file_location+i+'.xlsx'
        sheetNameIV = 'NBS NA PulseCut'
        df_RPV_iv = pd.read_excel(iv_file_location, sheet_name=sheetNameIV,header=None) #dataframe for voltammogram response
        df_RPV_iv_T = df_RPV_iv.T #transpose to get in correct format for sklearn
        X_iv = df_RPV_iv_T.to_numpy()
        results = myModel.predict(X_iv)
        #print(pd.DataFrame({"Sample": df_RPV_iv_T.index,"Predicted DA (uM)": np.round(results[:,0],4),"Predicted 5HT (uM)": np.round(results[:,1],4)}))
        df_results=pd.DataFrame({"Predicted DA (uM)": np.round(results[:,0],4),"Predicted 5HT (uM)": np.round(results[:,1],4)})
        #globals()['df_results_'+str(i[1:])+'_'+str(x)+'comp'] = df_results
        df_results.name = str(i[1:])+'_'+str(x)+'comp'
        current_dfs.append(df_results)
        predDA = df_results['Predicted DA (uM)'].to_numpy()
        predHT = df_results['Predicted 5HT (uM)'].to_numpy()
        
        stimType='Pulse'
        numComps=x
        print(i)
        mySampleName = i
        behavior=0

        objs.append(Sample(myName=i+'_' + preProcess + ' ' + modelChoice + ' ' + str(x) + ' Components ',
                           myData=df_results,myTime=i,myModel=modelChoice,myNumComp=x,myPreProcess=preProcess,
                           mySampleName=nameFiles[j]))
        
        if mySampleName in ['\\33850_P1','\\11250_P1_2']:
            objs[-1].set_StimFreq(40)
            objs[-1].set_StimLength(20)
        if mySampleName in ['\\25800_P1','\\27800_P1','\\9300_P1_2','\\13180_P1_2','\\16280_P1_2', '\\18000_P1_2','\\22080_P1_2','\\23800_P1_2','\\26400_P1_2','\\31900_P1_2','\\20100_postinj','\\22050_postinj','\\23950_postinj','\\28200_postinj','\\31550_postinj','\\33350_postinj','\\34900_postinj']:
            objs[-1].set_StimFreq(30)
            objs[-1].set_StimLength(20)
        if mySampleName in ['\\25800_P1','\\27800_P1','\\33850_P1']:
            objs[-1].set_Position(11803)
        if mySampleName in ['\\9300_P1_2','\\11250_P1_2', '\\13180_P1_2','\\16280_P1_2', '\\18000_P1_2']:
            objs[-1].set_Position(11950)
        if mySampleName in ['\\22080_P1_2','\\23800_P1_2', '\\26400_P1_2','\\31900_P1_2','\\20100_postinj','\\22050_postinj','\\23950_postinj','\\28200_postinj']:
            objs[-1].set_Position(12150)
        if mySampleName in ['\\33350_postinj','\\31550_postinj','\\34900_postinj']:
            objs[-1].set_Position(12350)
        if mySampleName in ['\\20100_postinj','\\22050_postinj','\\23950_postinj','\\28200_postinj','\\31550_postinj']:
            objs[-1].set_Drug('post-SSRI')
        if mySampleName not in ['\\20100_postinj', '\\22050_postinj', '\\23950_postinj', '\\28200_postinj']:
            objs[-1].set_Drug('pre-SSRI')
        
        fig, ax1 = plt.subplots()
        ax1.plot(time_array,1000*moving_average(predDA,10),label='Dopamine',color='b')
        #above was  time_array+7
        ax1.set_xlabel('Time Since Stimulation Start (s)',fontsize=16)
        ax1.tick_params(axis='x',labelsize=14)
        ax1.set_ylabel('Dopamine Concentration (nM)',fontSize=16)
        ax1.tick_params(axis='y',labelsize=14)
        ax2 = ax1.twinx() 
        ax2.plot(time_array,1000*moving_average(predHT,10),label='Serotinin',color='m')
        #above was  time_array+7
        ax2.axvspan(0, 20, color='gold', alpha=0.3)
        #ax2.legend(fontsize=12)
        ax2.set_xlabel('Time Since Stimulation Start (s)',fontsize=16)
        ax2.tick_params(axis='x',labelsize=14)
        ax2.set_ylabel('Serotonin Concentration (nM)',fontSize=16)
        ax2.tick_params(axis='y',labelsize=14)
        fig.legend(loc='upper right', bbox_to_anchor=(0.84, 0.85),fontsize=14)
        plt.title(str(i) + ' ' + preProcess + ' ' + modelChoice + ' ' + str(x) + ' Components ');
        fig = plt.gcf()
        fig.set_size_inches(9, 5)
        #plt.savefig(i[2:]+'1_3040_combined.jpeg',dpi=600,quality=95)
        plt.show()

        plt.plot(time_array,moving_average(predDA,10)-np.round(np.mean((df_results.iloc[:101,0]).to_numpy()),3),label='Dopamine',color='b');
        #above was  time_array+3
        plt.plot(time_array,moving_average(predHT,10)-np.round(np.mean((df_results.iloc[:101,1]).to_numpy()),3),label='Serotonin',color='m');
        #above was  time_array+3
        plt.axvspan(0, 20, color='gold', alpha=0.3);
        plt.legend(fontsize=12)
        plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
        plt.xticks(fontsize=12)
        plt.ylabel('Phasic Concentration, uM',fontSize=15)
        plt.yticks(fontsize=12)
        fig = plt.gcf()
        fig.set_size_inches(9, 5)
        plt.title(str(i) + ' ' + preProcess + ' ' + modelChoice + ' ' + str(x) + ' Components ');
        plt.show();
        j+=1

['Obj25800_P1', 'Obj27800_P1', 'Obj33850_P1', 'Obj9300_P1_2', 'Obj11250_P1_2', 'Obj13180_P1_2', 'Obj16280_P1_2', 'Obj18000_P1_2', 'Obj22080_P1_2', 'Obj23800_P1_2', 'Obj26400_P1_2', 'Obj31900_P1_2', 'Obj20100_postinj', 'Obj22050_postinj', 'Obj23950_postinj', 'Obj28200_postinj', 'Obj31550_postinj', 'Obj33350_postinj', 'Obj34900_postinj']
2
\25800_P1

\27800_P1

\33850_P1

\9300_P1_2

\11250_P1_2

\13180_P1_2

\16280_P1_2

\18000_P1_2

\22080_P1_2

\23800_P1_2


for i in objs:
    print(i.__dict__)

{'_Name': '\\25800_P1_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               1.8088              1.0523
1               1.8121              1.0535
2               1.8136              1.0533
3               1.8131              1.0536
4               1.8116              1.0535
..                 ...                 ...
496             1.8227              1.0528
497             1.8224              1.0530
498             1.8244              1.0534
499             1.8270              1.0537
500             1.8114              1.0517

[501 rows x 2 columns], '_Time': '\\25800_P1', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 11803, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj25800_P1', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\27800_P1_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               1.8078              1.0543
1               1.8143              1.0553
2               1.8150              1.0558
3               1.8177              1.0555
4               1.8166              1.0555
..                 ...                 ...
496             1.8264              1.0592
497             1.8252              1.0592
498             1.8232              1.0589
499             1.8200              1.0594
500             1.8105              1.0573

[501 rows x 2 columns], '_Time': '\\27800_P1', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 11803, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj27800_P1', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\33850_P1_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               1.8715              1.0801
1               1.8884              1.0815
2               1.8855              1.0823
3               1.8833              1.0822
4               1.8868              1.0817
..                 ...                 ...
496             1.9198              1.0820
497             1.9215              1.0817
498             1.9183              1.0818
499             1.9129              1.0815
500             1.9006              1.0808

[501 rows x 2 columns], '_Time': '\\33850_P1', '_StimType': 0, '_StimLength': 20, '_StimFreq': 40, '_Position': 11803, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj33850_P1', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\9300_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0328              1.0517
1               2.0436              1.0524
2               2.0416              1.0523
3               2.0389              1.0519
4               2.0377              1.0520
..                 ...                 ...
496             2.0539              1.0526
497             2.0552              1.0527
498             2.0562              1.0527
499             2.0567              1.0528
500             2.0374              1.0516

[501 rows x 2 columns], '_Time': '\\9300_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 11950, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj9300_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\11250_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0394              1.0522
1               2.0520              1.0526
2               2.0516              1.0526
3               2.0528              1.0529
4               2.0587              1.0536
..                 ...                 ...
496             2.0694              1.0528
497             2.0641              1.0524
498             2.0664              1.0524
499             2.0648              1.0527
500             2.0482              1.0516

[501 rows x 2 columns], '_Time': '\\11250_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 40, '_Position': 11950, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj11250_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\13180_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0544              1.0554
1               2.0682              1.0564
2               2.0668              1.0562
3               2.0719              1.0566
4               2.0694              1.0563
..                 ...                 ...
496             2.0857              1.0558
497             2.0832              1.0555
498             2.0849              1.0559
499             2.0845              1.0557
500             2.0728              1.0548

[501 rows x 2 columns], '_Time': '\\13180_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 11950, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj13180_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\16280_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0323              1.0498
1               2.0615              1.0529
2               2.0641              1.0529
3               2.0682              1.0533
4               2.0591              1.0522
..                 ...                 ...
496             2.0716              1.0526
497             2.0711              1.0528
498             2.0677              1.0524
499             2.0694              1.0525
500             2.0551              1.0512

[501 rows x 2 columns], '_Time': '\\16280_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 11950, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj16280_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\18000_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0610              1.0535
1               2.0704              1.0551
2               2.0695              1.0551
3               2.0707              1.0552
4               2.0667              1.0550
..                 ...                 ...
496             2.0703              1.0542
497             2.0798              1.0548
498             2.0786              1.0549
499             2.0708              1.0545
500             2.0511              1.0524

[501 rows x 2 columns], '_Time': '\\18000_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 11950, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj18000_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\22080_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0149              1.0771
1               2.0290              1.0783
2               2.0224              1.0782
3               2.0312              1.0786
4               2.0331              1.0787
..                 ...                 ...
496             2.0460              1.0769
497             2.0462              1.0771
498             2.0514              1.0777
499             2.0501              1.0773
500             2.0264              1.0751

[501 rows x 2 columns], '_Time': '\\22080_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12150, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj22080_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\23800_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0292              1.0762
1               2.0424              1.0777
2               2.0430              1.0776
3               2.0440              1.0776
4               2.0461              1.0778
..                 ...                 ...
496             2.0710              1.0770
497             2.0724              1.0773
498             2.0728              1.0772
499             2.0736              1.0771
500             2.0570              1.0761

[501 rows x 2 columns], '_Time': '\\23800_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12150, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj23800_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\26400_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0731              1.0780
1               2.0897              1.0797
2               2.0859              1.0792
3               2.0844              1.0793
4               2.0871              1.0799
..                 ...                 ...
496             2.1021              1.0802
497             2.1009              1.0799
498             2.1072              1.0806
499             2.1016              1.0803
500             2.0906              1.0795

[501 rows x 2 columns], '_Time': '\\26400_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12150, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj26400_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\31900_P1_2_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.0958              1.0796
1               2.1134              1.0804
2               2.1119              1.0804
3               2.1148              1.0805
4               2.1070              1.0797
..                 ...                 ...
496             2.1342              1.0803
497             2.1369              1.0806
498             2.1379              1.0805
499             2.1367              1.0800
500             2.1199              1.0791

[501 rows x 2 columns], '_Time': '\\31900_P1_2', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12150, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj31900_P1_2', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\20100_postinj_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.1555              1.0754
1               2.1666              1.0758
2               2.1669              1.0760
3               2.1678              1.0761
4               2.1708              1.0764
..                 ...                 ...
496             2.1835              1.0768
497             2.1844              1.0769
498             2.1813              1.0765
499             2.1846              1.0767
500             2.1674              1.0755

[501 rows x 2 columns], '_Time': '\\20100_postinj', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12150, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj20100_postinj', '_Drug': 'post-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\22050_postinj_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.1669              1.0761
1               2.1803              1.0771
2               2.1849              1.0773
3               2.1858              1.0774
4               2.1837              1.0780
..                 ...                 ...
496             2.1925              1.0767
497             2.1995              1.0774
498             2.1974              1.0773
499             2.1987              1.0773
500             2.1852              1.0763

[501 rows x 2 columns], '_Time': '\\22050_postinj', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12150, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj22050_postinj', '_Drug': 'post-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\23950_postinj_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.1882              1.0777
1               2.2051              1.0789
2               2.2030              1.0787
3               2.2059              1.0788
4               2.1938              1.0775
..                 ...                 ...
496             2.2044              1.0781
497             2.2076              1.0786
498             2.2045              1.0786
499             2.2054              1.0789
500             2.1767              1.0766

[501 rows x 2 columns], '_Time': '\\23950_postinj', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12150, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj23950_postinj', '_Drug': 'post-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\28200_postinj_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.1751              1.0788
1               2.1961              1.0801
2               2.1905              1.0796
3               2.1940              1.0802
4               2.1859              1.0793
..                 ...                 ...
496             2.1425              1.0555
497             2.1411              1.0551
498             2.1409              1.0553
499             2.1397              1.0549
500             2.1306              1.0533

[501 rows x 2 columns], '_Time': '\\28200_postinj', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12150, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj28200_postinj', '_Drug': 'post-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\31550_postinj_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.1666              1.0536
1               2.1769              1.0550
2               2.1763              1.0550
3               2.1762              1.0548
4               2.1698              1.0543
..                 ...                 ...
496             2.1818              1.0573
497             2.1840              1.0573
498             2.1824              1.0572
499             2.1811              1.0572
500             2.1710              1.0558

[501 rows x 2 columns], '_Time': '\\31550_postinj', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12350, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj31550_postinj', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\33350_postinj_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.1959              1.0609
1               2.1973              1.0610
2               2.1948              1.0609
3               2.1957              1.0610
4               2.1968              1.0609
..                 ...                 ...
496             2.2013              1.0609
497             2.2046              1.0607
498             2.2033              1.0608
499             2.1957              1.0605
500             2.2066              1.0601

[501 rows x 2 columns], '_Time': '\\33350_postinj', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12350, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj33350_postinj', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}
{'_Name': '\\34900_postinj_Normalize PCR 2 Components ', '_Data':      Predicted DA (uM)  Predicted 5HT (uM)
0               2.2250              1.0627
1               2.2150              1.0627
2               2.2152              1.0626
3               2.2334              1.0636
4               2.2352              1.0633
..                 ...                 ...
496             2.2284              1.0646
497             2.2286              1.0646
498             2.2259              1.0642
499             2.2079              1.0625
500             2.1969              1.0612

[501 rows x 2 columns], '_Time': '\\34900_postinj', '_StimType': 0, '_StimLength': 20, '_StimFreq': 30, '_Position': 12350, '_Behavior': 0, '_Model': 'PCR', '_NumComp': 2, '_PreProcess': 'Normalize', '_HyperParams': 0, '_SampleName': 'Obj34900_postinj', '_Drug': 'pre-SSRI', '_AlignedData': 0, '_BrainRegion': 0, '_5HT_Basal': 0, '_5HT_Phasic': 0, '_DA_Basal': 0, '_DA_Phasic': 0}


for i in objs:
    i._DA_Basal = i._Data['Predicted DA (uM)'].to_numpy()
    i._DA_Phasic = np.round(np.mean((i._Data['Predicted DA (uM)'].iloc[:101]).to_numpy()),3)
    i._5HT_Basal = i._Data['Predicted 5HT (uM)'].to_numpy()
    i._5HT_Phasic = np.round(np.mean((i._Data['Predicted 5HT (uM)'].iloc[:101]).to_numpy()),3)


for i in objs:
    plt.plot(i._DA_Basal,label=str(i._NumComp)+' Components '+str(i._SampleName))
plt.legend()
plt.show()

for i in objs:
    plt.plot(i._5HT_Basal,label=str(i._NumComp)+' Components '+str(i._SampleName))
plt.legend()
plt.show()

for j in stimulationFiles:
    for i in objs:
        if str(i._SampleName) == 'Obj'+j[1:]:
            plt.plot(i._5HT_Basal,label=str(i._NumComp)+' Components')
    plt.legend()
    plt.title(j+'Predicted Serotonin')
    plt.show()
    
for j in stimulationFiles:
    for i in objs:
        if str(i._SampleName) == 'Obj'+j[1:]:
            plt.plot(i._DA_Basal,label=str(i._NumComp)+' Components')
    plt.legend()
    plt.title(j+'Predicted Dopamine')
    plt.show()


for i in objs:
    if i._Position >= 12200:
        i._BrainRegion = 'Dorsal'
    else:
        i._BrainRegion = 'Ventral'
    if i._Time == '\\23950_postinj':
        i._StimType = 'None'


color_dict = {'11803':'r','11950':'g','12150':'b','12350':'m'}

for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000+11
                MA_Data = moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10)
                i._AlignedData = pd.DataFrame(MA_Data,time_array)
                plt.plot(time_array+11,MA_Data,label=i._BrainRegion+' '+str(i._Position-9000),color=color_dict[str(i._Position)])
plt.title('Predicted Striatal Dopamine, 2 Component '+modelChoice,fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 11803:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='r',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 2803, N=3',color='r')


listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 11950:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='g',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 2950, N=5',color='g')
plt.legend()


listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._StimType != 'None':
            if i._Position == 12150:
                listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='b',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 3150, N=7',color='b')
plt.legend()

listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 12350:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='m',alpha=0.1)
plt.plot(Average_byPosition,label='Dorsal 3350, N=3',color='m')
plt.legend()
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
plt.title('Predicted Average Striatal Dopamine, 2 Component '+modelChoice,fontsize=18)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array_offset = time_array_offset[0]
                time_array = (time_array - time_array_offset+20)*1*400/1000+11
                plt.plot(time_array,moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10),label=i._BrainRegion+' '+str(i._Position-9000),color=color_dict[str(i._Position)])
plt.title('Predicted Striatal Serotonin, 2 Component '+modelChoice,fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()


for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array_offset = time_array_offset[0]
                time_array = (time_array - time_array_offset+20)*1*400/1000+11
                MA_Data = moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)
                i._AlignedData = pd.DataFrame(MA_Data,time_array)
                plt.plot(time_array+11,MA_Data,label=i._BrainRegion+' '+str(i._Position-9000),color=color_dict[str(i._Position)])
plt.title('Predicted Striatal Serotonin, 2 Component '+modelChoice,fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 11803:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)

Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='r',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 2803, N=3',color='r')

listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 11950:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)

Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='g',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 2950, N=5',color='g')
plt.legend()


listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._StimType != 'None':
            if i._Position == 12150:
                listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)

Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='b',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 3150, N=7',color='b')
plt.legend()

listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 12350:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
print(Average_byPosition.to_string())

Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='m',alpha=0.1)
plt.plot(Average_byPosition,label='Dorsal 3350, N=3',color='m')
plt.legend()
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
plt.title('Predicted Average Striatal Serotonin, 2 Component '+modelChoice,fontsize=18)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()








for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==3:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000
                MA_Data = moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)
                i._AlignedData = pd.DataFrame(MA_Data,time_array)
                plt.plot(time_array+11,MA_Data,label=i._BrainRegion+' '+str(i._Position-9000),color=color_dict[str(i._Position)])
plt.title('Predicted Striatal Serotonin, 3 Component '+modelChoice,fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

listDF_byPosition=[]
for i in objs:
    if i._NumComp==3:
        if i._Position == 11803:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='r',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 2803, N=3',color='r')

listDF_byPosition=[]
for i in objs:
    if i._NumComp==3:
        if i._Position == 11950:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='g',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 2950, N=5',color='g')
plt.legend()


listDF_byPosition=[]
for i in objs:
    if i._NumComp==3:
        if i._StimType != 'None':
            if i._Position == 12150:
                listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='b',alpha=0.1)
plt.plot(Average_byPosition,label='Ventral 3150, N=7',color='b')
plt.legend()

listDF_byPosition=[]
for i in objs:
    if i._NumComp==3:
        if i._Position == 12350:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Std_byPosition = Average_byPosition.std(axis=1, level=0)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.fill_between(list(Average_byPosition.index.values),Average_byPosition[0]-Std_byPosition[0],Average_byPosition[0]+Std_byPosition[0],color='m',alpha=0.1)
plt.plot(Average_byPosition,label='Dorsal 3350, N=3',color='m')
plt.legend()
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
plt.title('Predicted Average Striatal Serotonin, 3 Component '+modelChoice,fontsize=18)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()










for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==3:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000
                plt.plot(time_array,moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10),label=i._BrainRegion+' '+str(i._Position-9000),color=color_dict[str(i._Position)])
plt.title('Predicted Striatal Serotonin, 3 Component '+modelChoice,fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

color_dict = {'pre-SSRI':'r','post-SSRI':'g'}
for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 200
                if int(time_array_offset[0]) <= 100:
                    time_array_offset = 200
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000
                plt.plot(time_array,moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10),label=i._Drug + str(i._StimFreq),color=color_dict[str(i._Drug)])
plt.title('Predicted Striatal Serotonin, 2 Component '+modelChoice,fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==3:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                if int(time_array_offset[0]) <= 100:
                    time_array_offset = 200
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000
                plt.plot(time_array,moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10),label=i._Drug + str(i._StimFreq),color=color_dict[str(i._Drug)])
plt.title('Predicted Striatal Serotonin, 3 Component'+modelChoice,fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

              0        0        0
-82.2       NaN      NaN  1.06267
-81.8       NaN      NaN  1.06255
-81.4       NaN      NaN  1.06254
-81.0       NaN      NaN  1.06251
-80.6       NaN      NaN  1.06237
-80.2       NaN      NaN  1.06225
-79.8       NaN      NaN  1.06217
-79.4       NaN      NaN  1.06221
-79.0       NaN      NaN  1.06220
-78.6       NaN      NaN  1.06221
-78.2       NaN      NaN  1.06221
-77.8       NaN      NaN  1.06219
-77.4       NaN      NaN  1.06218
-77.0       NaN      NaN  1.06228
-76.6       NaN      NaN  1.06217
-76.2       NaN      NaN  1.06220
-75.8       NaN  1.06079  1.06235
-75.4       NaN  1.06061  1.06235
-75.0       NaN  1.06056  1.06235
-74.6       NaN  1.06059  1.06235
-74.2       NaN  1.06057  1.06243
-73.8       NaN  1.06054  1.06248
-73.4       NaN  1.06060  1.06249
-73.0       NaN  1.06055  1.06242
-72.6       NaN  1.06048  1.06261
-72.2       NaN  1.06041  1.06270
-71.8       NaN  1.06035  1.06265
-71.4       NaN  1.06031  1.06259
-71.0       NaN  1.06031  1.06257
-70.6   1.05451  1.06024  1.06253
-70.2   1.05455  1.06022  1.06256
-69.8   1.05455  1.06023  1.06256
-69.4   1.05453  1.06008  1.06256
-69.0   1.05453  1.06008  1.06259
-68.6   1.05448  1.06015  1.06265
-68.2   1.05440  1.06019  1.06258
-67.8   1.05435  1.06025  1.06252
-67.4   1.05432  1.06047  1.06245
-67.0   1.05433  1.06057  1.06249
-66.6   1.05430  1.06071  1.06257
-66.2   1.05422  1.06085  1.06242
-65.8   1.05420  1.06099  1.06233
-65.4   1.05423  1.06113  1.06230
-65.0   1.05424  1.06123  1.06239
-64.6   1.05437  1.06130  1.06234
-64.2   1.05440  1.06144  1.06247
-63.8   1.05443  1.06148  1.06256
-63.4   1.05444  1.06140  1.06274
-63.0   1.05448  1.06141  1.06273
-62.6   1.05452  1.06140  1.06268
-62.2   1.05457  1.06141  1.06280
-61.8   1.05460  1.06144  1.06290
-61.4   1.05460  1.06138  1.06296
-61.0   1.05460  1.06143  1.06284
-60.6   1.05465  1.06150  1.06293
-60.2   1.05466  1.06151  1.06282
-59.8   1.05468  1.06154  1.06297
-59.4   1.05468  1.06153  1.06282
-59.0   1.05470  1.06155  1.06278
-58.6   1.05478  1.06155  1.06278
-58.2   1.05475  1.06151  1.06275
-57.8   1.05478  1.06145  1.06278
-57.4   1.05485  1.06152  1.06277
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 102.2  1.05767  1.06054  1.06300
 102.6  1.05768  1.06043  1.06273
 103.0  1.05763  1.06036  1.06254
 103.4  1.05760  1.06039  1.06233
 103.8  1.05768  1.06043  1.06238
 104.2  1.05762  1.06044  1.06249
 104.6  1.05760  1.06035  1.06256
 105.0  1.05755  1.06032  1.06262
 105.4  1.05756  1.06023  1.06270
 105.8  1.05769  1.06019  1.06285
 106.2  1.05765  1.06013  1.06300
 106.6  1.05764  1.06009  1.06337
 107.0  1.05766  1.06009  1.06362
 107.4  1.05769  1.06008  1.06388
 107.8  1.05765  1.06008  1.06389
 108.2  1.05768  1.06011  1.06387
 108.6  1.05768  1.06018  1.06390
 109.0  1.05769  1.06023  1.06395
 109.4  1.05766  1.06026  1.06403
 109.8  1.05755  1.06027  1.06405
 110.2  1.05757  1.06037  1.06406
 110.6  1.05759  1.06034  1.06408
 111.0  1.05758  1.06037  1.06416
 111.4  1.05760  1.06040  1.06421
 111.8  1.05764  1.06043  1.06422
 112.2  1.05760  1.06047  1.06426
 112.6  1.05762  1.06051  1.06428
 113.0  1.05765  1.06053  1.06427
 113.4  1.05776  1.06063  1.06420
 113.8  1.05772  1.06067  1.06399
 114.2  1.05768  1.06065  1.06378
 114.6  1.05765  1.06074      NaN
 115.0  1.05761  1.06065      NaN
 115.4  1.05759  1.06069      NaN
 115.8  1.05760  1.06070      NaN
 116.2  1.05754  1.06065      NaN
 116.6  1.05755  1.06064      NaN
 117.0  1.05749  1.06068      NaN
 117.4  1.05740  1.06066      NaN
 117.8  1.05741  1.06069      NaN
 118.2  1.05737  1.06075      NaN
 118.6  1.05731  1.06077      NaN
 119.0  1.05729  1.06088      NaN
 119.4  1.05727  1.06081      NaN
 119.8  1.05724  1.06079      NaN
 120.2  1.05727  1.06077      NaN
 120.6  1.05721  1.06083      NaN
 121.0  1.05713      NaN      NaN
 121.4  1.05707      NaN      NaN
 121.8  1.05707      NaN      NaN
 122.2  1.05708      NaN      NaN
 122.6  1.05711      NaN      NaN
 123.0  1.05707      NaN      NaN
 123.4  1.05701      NaN      NaN
 123.8  1.05695      NaN      NaN
 124.2  1.05696      NaN      NaN
 124.6  1.05695      NaN      NaN
 125.0  1.05700      NaN      NaN
 125.4  1.05702      NaN      NaN
 125.8  1.05694      NaN      NaN

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-35-5ade938462a0> in <module>
    252         if i._Position == 11803:
    253             listDF_byPosition.append(i._AlignedData)
--> 254 Average_byPosition=pd.concat(listDF_byPosition,axis=1)
    255 Std_byPosition = Average_byPosition.std(axis=1, level=0)
    256 Average_byPosition = Average_byPosition.mean(axis=1, level=0)

~\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
    253         verify_integrity=verify_integrity,
    254         copy=copy,
--> 255         sort=sort,
    256     )
    257 

~\Anaconda3\lib\site-packages\pandas\core\reshape\concat.py in __init__(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)
    302 
    303         if len(objs) == 0:
--> 304             raise ValueError("No objects to concatenate")
    305 
    306         if keys is None:

ValueError: No objects to concatenate


color_dict = {'11803':'r','11950':'g','12150':'b','12350':'m'}

for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array_offset = time_array_offset[0]
                time_array = (time_array - time_array_offset+20)*1*400/1000
                MA_Data = moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)
                i._AlignedData = pd.DataFrame(MA_Data,time_array)
                plt.plot(time_array,MA_Data,label=i._BrainRegion+' '+str(i._Position-9000),color=color_dict[str(i._Position)])
plt.title('Predicted Striatal Serotonin, 2 Component PLSR',fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 11803:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.plot(Average_byPosition,label='11803')

listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 11950:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.plot(Average_byPosition,label='11950')
plt.legend()


listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._StimType != 'None':
            if i._Position == 12150:
                listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.plot(Average_byPosition,label='12150')
plt.legend()

listDF_byPosition=[]
for i in objs:
    if i._NumComp==2:
        if i._Position == 12350:
            listDF_byPosition.append(i._AlignedData)
Average_byPosition=pd.concat(listDF_byPosition,axis=1)
Average_byPosition = Average_byPosition.mean(axis=1, level=0)
plt.plot(Average_byPosition,label='12350')
plt.legend()
plt.show()


color_dict = {'11803':'r','11950':'g','12150':'b','12350':'m'}

listofDFs_PhasicHT1=[]
for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000+11
                listofDFs_PhasicHT1.append(pd.DataFrame((moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)-np.round(np.mean((i._Data['Predicted 5HT (uM)'].iloc[:101]).to_numpy()),3)),time_array))
                plt.plot(time_array,(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)-np.round(np.mean((i._Data['Predicted 5HT (uM)'].iloc[:101]).to_numpy()),3)),label=i._BrainRegion+' '+str(i._Position-9000),color=color_dict[str(i._Position)])
plt.title('Predicted Striatal Serotonin Phasic, 2 Component PLSR',fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Phasic Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

listofDFs_PhasicDA1=[]
for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000+11
                listofDFs_PhasicDA1.append(pd.DataFrame((moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10)-np.round(np.mean((i._Data['Predicted DA (uM)'].iloc[:101]).to_numpy()),3)),time_array))
                plt.plot(time_array,(moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10)-np.round(np.mean((i._Data['Predicted DA (uM)'].iloc[:101]).to_numpy()),3)),label=i._BrainRegion+' '+str(i._Position-9000),color=color_dict[str(i._Position)])
plt.title('Predicted Striatal Dopamine Phasic, 2 Component PLSR',fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Phasic Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()


color_dict = {'pre-SSRI':'r','post-SSRI':'g'}
listofDFs_PhasicHT2=[]
for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000+11
                listofDFs_PhasicHT2.append(pd.DataFrame((moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)-np.round(np.mean((i._Data['Predicted 5HT (uM)'].iloc[:101]).to_numpy()),3)),time_array))
                plt.plot(time_array,(moving_average(i._Data['Predicted 5HT (uM)'].to_numpy(),10)-np.round(np.mean((i._Data['Predicted 5HT (uM)'].iloc[:101]).to_numpy()),3)),label=i._Drug + str(i._StimFreq),color=color_dict[str(i._Drug)])
plt.title('Predicted Striatal Serotonin Phasic, 2 Component PLSR',fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Phasic Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

listofDFs_PhasicDA2=[]
for j in stimulationFiles:
    for i in objs:
        if i._StimType != 'None':
            if str(i._SampleName) == 'Obj'+j[1:] and i._NumComp==2:
                m = max(moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10))
                time_array_offset=([k for k, l in enumerate(moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10)) if l == m])
                if int(time_array_offset[0]) >= 300:
                    time_array_offset = 230
                time_array = np.arange(0,492)
                time_array = (time_array - time_array_offset+20)*1*400/1000+11
                listofDFs_PhasicDA2.append(pd.DataFrame((moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10)-np.round(np.mean((i._Data['Predicted DA (uM)'].iloc[:101]).to_numpy()),3)),time_array))
                plt.plot(time_array,(moving_average(i._Data['Predicted DA (uM)'].to_numpy(),10)-np.round(np.mean((i._Data['Predicted DA (uM)'].iloc[:101]).to_numpy()),3)),label=i._Drug + str(i._StimFreq),color=color_dict[str(i._Drug)])
plt.title('Predicted Striatal Dopamine Phasic, 2 Component PLSR',fontsize=18)
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Phasic Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()


listDF_byFreq=[]
for i in objs:
    if i._NumComp==2:
        if i._StimFreq == 40:
            listDF_byFreq.append(i._AlignedData-i._AlignedData[:51].mean(axis=1, level=0)[0])
Average_byFreq=pd.concat(listDF_byFreq,axis=1)
Std_byFreq = Average_byFreq.std(axis=1, level=0)
Average_byFreq = Average_byFreq.mean(axis=1, level=0)
plt.fill_between(list(Average_byFreq.index.values),Average_byFreq[0]-Std_byFreq[0],Average_byFreq[0]+Std_byFreq[0],color='b',alpha=0.1)
plt.plot(Average_byFreq,label='40 Hz, N=2',color='b')

listDF_byFreq=[]
for i in objs:
    if i._NumComp==2:
        if i._StimFreq == 30:
            if i._StimType != 'None':
                listDF_byFreq.append(i._AlignedData-i._AlignedData[:51].mean(axis=1, level=0)[0])
Average_byFreq=pd.concat(listDF_byFreq,axis=1)
Std_byFreq = Average_byFreq.std(axis=1, level=0)
Average_byFreq = Average_byFreq.mean(axis=1, level=0)
plt.fill_between(list(Average_byFreq.index.values),Average_byFreq[0]-Std_byFreq[0],Average_byFreq[0]+Std_byFreq[0],color='r',alpha=0.1)
plt.plot(Average_byFreq,label='30 Hz, N=2',color='r')

plt.legend()
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
plt.title('Predicted Average Striatal Serotonin, 3 Component PLSR',fontsize=18)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()


df_PhasicHT1=pd.concat(listofDFs_PhasicHT1,axis=1)
df_PhasicHT1_Std=df_PhasicHT1.std(axis=1, level=0)
df_PhasicHT1_only_preSSRI=df_PhasicHT1.iloc[:,8:12]
df_PhasicHT1_only_preSSRI_Std=df_PhasicHT1_only_preSSRI.std(axis=1, level=0)
df_PhasicHT1_only_postSSRI=df_PhasicHT1.iloc[:,12:15]
df_PhasicHT1_only_postSSRI_Std=df_PhasicHT1_only_postSSRI.std(axis=1, level=0)

df_PhasicDA1=pd.concat(listofDFs_PhasicDA1,axis=1)
df_PhasicDA1_Std=df_PhasicDA1.std(axis=1, level=0)
df_PhasicDA1_only_preSSRI=df_PhasicDA1.iloc[:,8:12]
df_PhasicDA1_only_preSSRI_Std=df_PhasicDA1_only_preSSRI.std(axis=1, level=0)
df_PhasicDA1_only_postSSRI=df_PhasicDA1.iloc[:,12:15]
df_PhasicDA1_only_postSSRI_Std=df_PhasicDA1_only_postSSRI.std(axis=1, level=0)

plt.plot(df_PhasicHT1.mean(axis=1, level=0),label='HT (N=7)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,554), (df_PhasicHT1.mean(axis=1, level=0)-df_PhasicHT1_Std)[0],(df_PhasicHT1.mean(axis=1, level=0)+df_PhasicHT1_Std)[0],color='b',alpha=0.1)
plt.plot(df_PhasicDA1.mean(axis=1, level=0),label='DA (N=7)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,555), (df_PhasicDA1.mean(axis=1, level=0)-df_PhasicDA1_Std)[0],(df_PhasicDA1.mean(axis=1, level=0)+df_PhasicDA1_Std)[0],color='r',alpha=0.1)
plt.title("Average All Phasic Release, Pos 3150",fontsize=18)
plt.legend()
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Phasic Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

plt.plot(df_PhasicHT1_only_preSSRI.mean(axis=1, level=0),label='preSSRI (N=4)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,554), (df_PhasicHT1_only_preSSRI.mean(axis=1, level=0)-df_PhasicHT1_only_preSSRI_Std)[0],(df_PhasicHT1_only_preSSRI.mean(axis=1, level=0)+df_PhasicHT1_only_preSSRI_Std)[0],color='b',alpha=0.1)
plt.plot(df_PhasicHT1_only_postSSRI.mean(axis=1, level=0),label='postSSRI (N=3)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,554), (df_PhasicHT1_only_postSSRI.mean(axis=1, level=0)-df_PhasicHT1_only_postSSRI_Std)[0],(df_PhasicHT1_only_postSSRI.mean(axis=1, level=0)+df_PhasicHT1_only_postSSRI_Std)[0],color='r',alpha=0.1)
plt.title("Average 5HT Phasic Release, Pos 3150",fontsize=18)
plt.legend()
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Phasic Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()

plt.plot(df_PhasicDA1_only_preSSRI.mean(axis=1, level=0),label='preSSRI (N=4)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,555), (df_PhasicDA1_only_preSSRI.mean(axis=1, level=0)-df_PhasicDA1_only_preSSRI_Std)[0],(df_PhasicDA1_only_preSSRI.mean(axis=1, level=0)+df_PhasicDA1_only_preSSRI_Std)[0],color='b',alpha=0.1)
plt.plot(df_PhasicDA1_only_postSSRI.mean(axis=1, level=0),label='postSSRI (N=3)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,555), (df_PhasicDA1_only_postSSRI.mean(axis=1, level=0)-df_PhasicDA1_only_postSSRI_Std)[0],(df_PhasicDA1_only_postSSRI.mean(axis=1, level=0)+df_PhasicDA1_only_postSSRI_Std)[0],color='r',alpha=0.1)
plt.title("Average DA Phasic Release, Pos 3150",fontsize=18)
plt.legend()
plt.axvspan(0, 20, color='gold', alpha=0.3);
plt.xlabel('Time Since Stimulation Start (s)',fontsize=15)
plt.xticks(fontsize=12)
plt.ylabel('Phasic Concentration, uM',fontSize=15)
plt.yticks(fontsize=12)
fig = plt.gcf()
fig.set_size_inches(9, 5)
#removes duplicates from legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.show()


df_PhasicHT1=pd.concat(listofDFs_PhasicHT1,axis=1)
df_PhasicHT1_Std=df_PhasicHT1.std(axis=1, level=0)
df_PhasicHT1_only_preSSRI=df_PhasicHT1.iloc[:,:12]
df_PhasicHT1_only_preSSRI_Std=df_PhasicHT1_only_preSSRI.std(axis=1, level=0)
df_PhasicHT1_only_postSSRI=df_PhasicHT1.iloc[:,12:]
df_PhasicHT1_only_postSSRI_Std=df_PhasicHT1_only_postSSRI.std(axis=1, level=0)

df_PhasicDA1=pd.concat(listofDFs_PhasicDA1,axis=1)
df_PhasicDA1_Std=df_PhasicDA1.std(axis=1, level=0)
df_PhasicDA1_only_preSSRI=df_PhasicDA1.iloc[:,:12]
df_PhasicDA1_only_preSSRI_Std=df_PhasicDA1_only_preSSRI.std(axis=1, level=0)
df_PhasicDA1_only_postSSRI=df_PhasicDA1.iloc[:,12:]
df_PhasicDA1_only_postSSRI_Std=df_PhasicDA1_only_postSSRI.std(axis=1, level=0)

plt.plot(df_PhasicHT1.mean(axis=1, level=0),label='HT (N=18)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,554), (df_PhasicHT1.mean(axis=1, level=0)-df_PhasicHT1_Std)[0],(df_PhasicHT1.mean(axis=1, level=0)+df_PhasicHT1_Std)[0],color='b',alpha=0.1)
plt.plot(df_PhasicDA1.mean(axis=1, level=0),label='DA (N=18)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,555), (df_PhasicDA1.mean(axis=1, level=0)-df_PhasicDA1_Std)[0],(df_PhasicDA1.mean(axis=1, level=0)+df_PhasicDA1_Std)[0],color='r',alpha=0.1)
plt.title("Average All Phasic Release")
plt.legend()
plt.show()

plt.plot(df_PhasicHT1_only_preSSRI.mean(axis=1, level=0),label='preSSRI (N=12)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,554), (df_PhasicHT1_only_preSSRI.mean(axis=1, level=0)-df_PhasicHT1_only_preSSRI_Std)[0],(df_PhasicHT1_only_preSSRI.mean(axis=1, level=0)+df_PhasicHT1_only_preSSRI_Std)[0],color='b',alpha=0.1)
plt.plot(df_PhasicHT1_only_postSSRI.mean(axis=1, level=0),label='postSSRI (N=6)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,554), (df_PhasicHT1_only_postSSRI.mean(axis=1, level=0)-df_PhasicHT1_only_postSSRI_Std)[0],(df_PhasicHT1_only_postSSRI.mean(axis=1, level=0)+df_PhasicHT1_only_postSSRI_Std)[0],color='r',alpha=0.1)
plt.title("Average 5HT Phasic Release")
plt.legend()
plt.show()

plt.plot(df_PhasicDA1_only_preSSRI.mean(axis=1, level=0),label='preSSRI (N=12)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,555), (df_PhasicDA1_only_preSSRI.mean(axis=1, level=0)-df_PhasicDA1_only_preSSRI_Std)[0],(df_PhasicDA1_only_preSSRI.mean(axis=1, level=0)+df_PhasicDA1_only_preSSRI_Std)[0],color='b',alpha=0.1)
plt.plot(df_PhasicDA1_only_postSSRI.mean(axis=1, level=0),label='postSSRI (N=6)')
plt.fill_between(np.linspace(-97.2+11,124.4+11,555), (df_PhasicDA1_only_postSSRI.mean(axis=1, level=0)-df_PhasicDA1_only_postSSRI_Std)[0],(df_PhasicDA1_only_postSSRI.mean(axis=1, level=0)+df_PhasicDA1_only_postSSRI_Std)[0],color='r',alpha=0.1)
plt.title("Average DA Phasic Release")
plt.legend()
plt.show()


for i in objs:
    if i._NumComp == 2:
        if i._StimFreq == 40:
            --
        if i_StimFreq == 30: